---
title: "Who cites Muhan Zhang — 150 papers of our library"
description: "150 papers of the Elamur Research library cite the works of Muhan Zhang, found in their bibliographies; the most cited first."
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---
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# Who cites Muhan Zhang

151 citations of their works from 150 papers of our library (of the 149,439 whose bibliographies we read) · 31 with a page here, the most cited first · [← Muhan Zhang](https://md.research.elamur.ai/free/en/authors/muhan-zhang/)

1. [**RULER: What's the Real Context Size of Your Long-Context Language Models? arXiv:2404.06654**Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, et al.*2024 · 400 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2024-ruler-s-real-context-size/)
2. **[Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models](https://md.research.elamur.ai/free/en/papers/levy-2024-same-task-more-tokens-impact/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mosh Levy, Alon Jacoby, Yoav Goldberg*2024 · 217 citations*
3. [**LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression arXiv:2310.06839**Huiqiang Jiang, Qianhui Wu, Xufang Luo, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-longllmlingua-accelerating-enhancing-llms-long/)
4. [**Long-context LLMs Struggle with Long In-context Learning arXiv:2404.02060**Tianle Li, Ge Zhang, Quy Duc Do, et al.*2024 · 138 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-long-context-llms-struggle-long/)
5. [**LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks arXiv:2412.15204**Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longbench-v2-deeper-understanding/)
6. **[The Impact of Reasoning Step Length on Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2024-impact-reasoning-step-length-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingyu Jin, Qinkai Yu, Dong Shu, et al.*2024 · 119 citations*
7. **[ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://md.research.elamur.ai/free/en/papers/bian-2024-chatgpt-is-knowledgeable-but-inexperienced/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ning Bian, Xianpei Han, Le Sun, et al.*2024 · 106 citations*
8. [**LongAlign: A Recipe for Long Context Alignment of Large Language Models arXiv:2401.18058**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2024 · 98 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longalign-recipe-long-context/)
9. **[In-Context Learning with Long-Context Models: An In-Depth Exploration](https://md.research.elamur.ai/free/en/papers/bertsch-2025-context-learning-long-context-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Amanda Bertsch, Maor Ivgi, Emily Xiao, et al.*2025 · 81 citations*
10. **[Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA](https://md.research.elamur.ai/free/en/papers/wang-2024-leave-no-document-behind-benchmarking/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Minzheng Wang, Longze Chen, Fu Cheng, et al.*2024 · 72 citations*
11. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
12. [**LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K arXiv:2402.05136**Tao Yuan, Xuefei Ning, Dong Zhou, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-lv-eval-balanced-long-context/)
13. [**BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models arXiv:2309.06085**Wei Qi Leong, Jian Gang Ngui, Yosephine Susanto, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/leong-2023-bhasa-holistic-southeast-asian-linguistic/)
14. [**NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens arXiv:2403.12766**Cunxiang Wang, Ruoxi Ning, Boqi Pan, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-novelqa-benchmarking-question-answering-documents/)
15. **[Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens](https://md.research.elamur.ai/free/en/papers/zhao-2026-is-chain-thought-reasoning-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengshuai Zhao, Zhen Tan, Pingchuan Ma, et al.*2026 · 21 citations*
16. **[Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models](https://md.research.elamur.ai/free/en/papers/song-2025-counting-stars-multi-evidence-position/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mingyang Song, Mao Zheng, Xuan Luo*2025 · 20 citations*
17. **[Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs](https://md.research.elamur.ai/free/en/papers/yang-2025-code-think-think-code-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dayu Yang, Tianyang Liu, Daoan Zhang, et al.*2025 · 19 citations*
18. **[RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation](https://md.research.elamur.ai/free/en/papers/li-2025-rethinkmcts-refining-erroneous-thoughts-monte/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qingyao Li, Wei Xia, Xinyi Dai, et al.*2025 · 16 citations*
19. **[TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review](https://md.research.elamur.ai/free/en/papers/chang-2025-treereview-dynamic-tree-questions-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuan Chang, Ziyue Li, Hengyuan Zhang, et al.*2025 · 16 citations*
20. [**CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning arXiv:2503.13517**Hao Cui, Zahra Shamsi, Gowoon Cheon, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-curie-evaluating-llms-multitask-scientific/)
21. **[LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios](https://md.research.elamur.ai/free/en/papers/wu-2025-lifbench-evaluating-instruction-following-performance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaodong Wu, Minhao Wang, Yichen Liu, et al.*2025 · 11 citations*
22. **[MDCure: A Scalable Pipeline for Multi-Document Instruction-Following](https://md.research.elamur.ai/free/en/papers/liu-2025-mdcure-scalable-pipeline-multi-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, et al.*2025 · 11 citations*
23. **[LLM Agents in Law: Taxonomy, Applications, and Challenges](https://md.research.elamur.ai/free/en/papers/liu-2026-llm-agents-law-taxonomy-applications/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuang Liu, Ruijia Zhang, Ruoyun Ma, et al.*2026 · 4 citations*
24. **[SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems](https://md.research.elamur.ai/free/en/papers/zhang-2026-silo-bench-scalable-environment-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuzhe Zhang, Feiran Liu, Yi Shan, et al.*2026 · 2 citations*
25. **[H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents](https://md.research.elamur.ai/free/en/papers/ye-2026-h-mem-hybrid-multi-dimensional/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zihe Ye, Jingyuan Huang, Weixin Chen, et al.*2026 · 1 citation*
26. **[KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation](https://md.research.elamur.ai/free/en/papers/tatarinov-2026-kg-mulqa-framework-kg-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa, et al.*2026 · 1 citation*
27. **[Data Foundations of Long-Context Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/sun-2026-data-foundations-long-context-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zechen Sun, Yuyang Sun, Zhaochen Su, et al.*2026*
28. **[Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation](https://md.research.elamur.ai/free/en/papers/tang-2026-knowledge-is-not-enough-injecting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pingzhi Tang, Yiding Wang, Muhan Zhang*2026*
29. **[LLM Multi-Agent Systems for Long Triple Set Data-to-Text Generation](https://md.research.elamur.ai/free/en/papers/osuji-2026-llm-multi-agent-systems-long/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chinonso Cynthia Osuji, Simon Mille, Mark Andrade, et al.*2026*
30. **[NeedleChain: Measuring Intact Context Comprehension Capability of Large Language Models](https://md.research.elamur.ai/free/en/papers/moon-2026-needlechain-measuring-intact-context-comprehension/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hyeonseok Moon, Heuiseok Lim*2026*
31. **[Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2026-shuttle-between-symbolic-instructions-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wangtao Sun, Haotian Xu, Huanxuan Liao, et al.*2026*

119 more citing papers of our library have no page here yet.

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